Tasks and their role in visual neuroscience

Adversarial testing of global neuronal workspace and integrated information theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, but have not been directly compared3. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace theory (GNWT)6-10 via a theory-neutral consortium11-13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof12. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here14-17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.

Anatomically distinct regions in the inferior frontal cortex are modulated by task and reading skill

Inferior frontal cortex (IFC) is a critical region for reading and language. This part of the cortex is highly heterogeneous in its structural and functional organization and shows high variability across individuals. Despite decades of research, the relationship between specific IFC regions and reading skill remains unclear. To shed light on the function of IFC in reading, we aim to (1) characterize the functional landscape of text-selective responses in IFC, while accounting for interindividual variability; and (2) examine how text-selective regions in the IFC relate to reading proficiency. To this end, children with a wide range of reading ability (N=66; age 7-14 years, 34 female, 32 male) completed functional MRI scans while performing two tasks on text and non-text visual stimuli. Importantly, both tasks do not explicitly require reading, and can be performed on all visual stimuli. This design allows us to tease apart stimulus-driven responses from task-driven responses and examine where in IFC task and stimulus interact. We were able to identify three anatomically-distinct, text-selective clusters of activation in IFC, in the inferior frontal sulcus (IFS), and dorsal and ventral precentral gyrus (PrG). ​​These three regions showed a strong task effect that was highly specific to text. Furthermore, text-selectivity in the IFS and dorsal PrG was associated with reading proficiency, such that better readers showed higher selectivity to text. These findings suggest that text-selective regions in the IFC are sensitive to both stimulus and task, and highlight the importance of this region for proficient reading.Significance statement The inferior frontal cortex (IFC) is a critical region for language processing, yet despite decades of research, its relationship with reading skill remains unclear. In a group of children with a wide range of reading skills, we were able to identify three anatomically distinct text-selective clusters of activation in the IFC. ​​These regions showed a strong task effect that was highly selective to text. Text-selectivity was positively correlated with reading proficiency, such that better readers showed higher selectivity to text, even in tasks that did not require reading. These findings suggest that multiple text-selective regions within IFC are sensitive to both stimulus and task, and highlight the critical role of IFC for reading proficiency.

Spatial Frequency Maps in Human Visual Cortex: A Replication and Extension

In a step toward developing a model of human primary visual cortex, a recent study introduced a model of spatial frequency tuning in V1 (Broderick, Simoncelli, & Winawer, 2022). The model is compact, using just 9 parameters to predict BOLD response amplitude for locations across all of V1 as a function of stimulus orientation and spatial frequency. Here we replicated this analysis in a new dataset, the 'nsdsynthetic' supplement to the Natural Scenes Dataset (Allen et al., 2022), to assess generalization of model parameters. Furthermore, we extended the analyses to extrastriate maps V2 and V3. For each retinotopic map in the 8 NSD subjects, we fit the 9-parameter model. Despite many experimental differences between NSD and the original study, including stimulus size, experimental design, and MR field strength, there was good agreement in most model parameters. The dependence of preferred spatial frequency on eccentricity in V1 was similar between NSD and Broderick et al. Moreover, the effect of absolute stimulus orientation on spatial frequency maps was similar: higher preferred spatial frequency for horizontal and cardinal orientations compared to vertical and oblique orientations in both studies. The extension to extrastriate maps revealed that the biggest change in tuning between maps was in bandwidth: the bandwidth in spatial frequency tuning increased by 70% from V1 to V2 and 100% from V1 to V3, paralleling known increases in receptive field size. Together, the results show robust reproducibility and bring us closer to a systematic characterization of spatial encoding in the human visual system.

Task-modulated neural responses in scene-selective regions of the human brain

The study of scene perception is crucial to the understanding of how one interprets and interacts with their environment, and how the environment impacts various cognitive functions. The literature so far has mainly focused on the impact of low-level and categorical properties of scenes and how they are represented in the scene-selective regions in the brain, PPA, RSC, and OPA. However, higher-level scene perception and the impact of behavioral goals is a developing research area. Moreover, the selection of the stimuli has not been systematic and mainly focused on outdoor environments. In this fMRI experiment, we adopted multiple behavioral tasks, selected real-life indoor stimuli with a systematic categorization approach, and used various multivariate analysis techniques to explain the neural modulation of scene perception in the scene-selective regions of the human brain. Participants (N = 21) performed categorization and approach-avoidance tasks during fMRI scans while they were viewing scenes from built environment categories based on different affordances ((i)access and (ii)circulation elements, (iii)restrooms and (iv)eating/seating areas). ROI-based classification analysis revealed that the OPA was significantly successful in decoding scene category regardless of the task, and that the task condition affected category decoding performances of all the scene-selective regions. Model-based representational similarity analysis (RSA) revealed that the activity patterns in scene-selective regions are best explained by task. These results contribute to the literature by extending the task and stimulus content of scene perception research, and uncovering the impact of behavioral goals on the scene-selective regions of the brain.

Flexibility of intrinsic neural timescales during distinct behavioral states

Recent neuroimaging studies demonstrate a heterogeneity of timescales prevalent in the brain's ongoing spontaneous activity, labeled intrinsic neural timescales (INT). At the same time, neural timescales also reflect stimulus- or task-related activity. The relationship of the INT during the brain's spontaneous activity with their involvement in task states including behavior remains unclear. To address this question, we combined calcium imaging data of spontaneously behaving mice and human electroencephalography (EEG) during rest and task states with computational modeling. We obtained four primary findings: (i) the distinct behavioral states can be accurately predicted from INT, (ii) INT become longer during behavioral states compared to rest, (iii) INT change from rest to task is correlated negatively with the variability of INT during rest, (iv) neural mass modeling shows a key role of recurrent connections in mediating the rest-task change of INT. Extending current findings, our results show the dynamic nature of the brain's INT in reflecting continuous behavior through their flexible rest-task modulation possibly mediated by recurrent connections.

Real Face Value: The Processing of Naturalistic Facial Expressions in the Macaque Inferior Temporal Cortex

For primates, expressions of fear are thought to be powerful social signals. In laboratory settings, faces with fearful expressions have reliably evoked valence effects in inferior temporal cortex. However, because macaques use so called "fear grins" in a variety of different contexts, the deeper question is whether the macaque inferior temporal cortex is tuned to the prototypical fear grin, or to conspecifics signaling fear? In this study, we combined neuroimaging with the results of a behavioral task to investigate how macaques encode a wide variety of fearful facial expressions. In Experiment 1, we identified two sets of macaque face stimuli using different approaches; we selected faces based on the emotional context (i.e., calm vs. fearful), and we selected faces based on the engagement of action units (i.e., neutral vs. fear grins). We also included human faces in Experiment 1. Then, using fMRI, we found that the faces selected based on context elicited a larger valence effect in the inferior temporal cortex than faces selected based on visual appearance. Furthermore, human facial expressions only elicited weak valence effects. These observations were further supported by the results of a two-alternative, forced-choice task (Experiment 2), suggesting that fear grins vary in their perceived pleasantness. Collectively, these findings indicate that the macaque inferior temporal cortex is more involved in social intelligence than commonly assumed, encoding emergent properties in naturalistic face stimuli that transcend basic visual features. These results demand a rethinking of theories surrounding the function and operationalization of primate inferior temporal cortex.

Maintenance of Bodily Expressions Modulates Functional Connectivity Between Prefrontal Cortex and Extrastriate Body Area During Working Memory Processing

Background/Objectives: As a form of visual input, bodily expressions can be maintained and manipulated in visual working memory (VWM) over a short period of time. While the prefrontal cortex (PFC) plays an indispensable role in top-down control, it remains largely unclear whether this region also modulates the VWM storage of bodily expressions during a delay period. Therefore, the two primary goals of this study were to examine whether the emotional bodies would elicit heightened brain activity among areas such as the PFC and extrastriate body area (EBA) and whether the emotional effects subsequently modulate the functional connectivity patterns for active maintenance during delay periods. Methods: During functional magnetic resonance imaging (fMRI) scanning, participants performed a delayed-response task in which they were instructed to view and maintain a body stimulus in working memory before emotion categorization (happiness, anger, and neutral). If processing happy and angry bodies consume increased cognitive demands, stronger PFC activation and its functional connectivity with perceptual areas would be observed. Results: Results based on univariate and multivariate analyses conducted on the data collected during stimulus presentation revealed an enhanced processing of the left PFC and left EBA. Importantly, subsequent functional connectivity analyses performed on delayed-period data using a psychophysiological interaction model indicated that functional connectivity between the PFC and EBA increases for happy and angry bodies compared to neutral bodies. Conclusions: The emotion-modulated coupling between the PFC and EBA during maintenance deepens our understanding of the functional organization underlying the VWM processing of bodily information.

Principles of intensive human neuroimaging

The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in human neuroscience has focused on acquiring either a few hours of data on many individuals ('wide' fMRI) or many hours of data on a few individuals ('deep' fMRI). In this opinion article, we highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: one that strives for extensive sampling of cognitive phenomena to support computational modeling and detailed investigation of brain function at the single voxel level. We discuss the fundamental principles, trade-offs, and practical considerations of intensive fMRI. We also emphasize that intensive fMRI does not simply mean collecting more data: it requires careful design of experiments to enable a rich hypothesis space, optimizing data quality, and strategically curating public resources to maximize community impact.

Towards an ecologically valid naturalistic cognitive neuroscience of memory and event cognition

The landscape of human memory and event cognition research has witnessed a transformative journey toward the use of naturalistic contexts and tasks. In this review, we track this progression from abrupt, artificial stimuli used in extensively controlled laboratory experiments to more naturalistic tasks and stimuli that present a more faithful representation of the real world. We argue that in order to improve ecological validity, naturalistic study designs must consider the complexity of the cognitive phenomenon being studied. Then, we review the current state of "naturalistic" event segmentation studies and critically assess frequently employed movie stimuli. We evaluate recently developed tools like lifelogging and other extended reality technologies to help address the challenges we identified with existing naturalistic approaches. We conclude by offering some guidelines that can be used to design ecologically valid cognitive neuroscience studies of memory and event cognition.

Centering cognitive neuroscience on task demands and generalization

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors

To interpret our surroundings, the brain uses a visual categorization process. Current theories and models suggest that this process comprises a hierarchy of different computations that transforms complex, high-dimensional inputs into lower-dimensional representations (i.e., manifolds) in support of multiple categorization behaviors. Here, we tested this hypothesis by analyzing these transformations reflected in dynamic MEG source activity while individual participants actively categorized the same stimuli according to different tasks: face expression, face gender, pedestrian gender, and vehicle type. Results reveal three transformation stages guided by the pre-frontal cortex. At stage 1 (high-dimensional, 50-120 ms), occipital sources represent both task-relevant and task-irrelevant stimulus features; task-relevant features advance into higher ventral/dorsal regions, whereas task-irrelevant features halt at the occipital-temporal junction. At stage 2 (121-150 ms), stimulus feature representations reduce to lower-dimensional manifolds, which then transform into the task-relevant features underlying categorization behavior over stage 3 (161-350 ms). Our findings shed light on how the brain's network mechanisms transform high-dimensional inputs into specific feature manifolds that support multiple categorization behaviors.

Convolutional neural network models applied to neuronal responses in macaque V1 reveal limited nonlinear processing

Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Gabor filters followed by simple nonlinearities. However, recent work employing convolutional neural network (CNN) models has suggested that V1 relies on far more nonlinear computations than previously thought. Specifically, unit responses in an intermediate layer of VGG-19 were found to best predict macaque V1 responses to thousands of natural and synthetic images. Here, we evaluated the hypothesis that the poor performance of lower layer units in VGG-19 might be attributable to their small receptive field size rather than to their lack of complexity per se. We compared VGG-19 with AlexNet, which has much larger receptive fields in its lower layers. Whereas the best-performing layer of VGG-19 occurred after seven nonlinear steps, the first convolutional layer of AlexNet best predicted V1 responses. Although the predictive accuracy of VGG-19 was somewhat better than that of standard AlexNet, we found that a modified version of AlexNet could match the performance of VGG-19 after only a few nonlinear computations. Control analyses revealed that decreasing the size of the input images caused the best-performing layer of VGG-19 to shift to a lower layer, consistent with the hypothesis that the relationship between image size and receptive field size can strongly affect model performance. We conducted additional analyses using a Gabor pyramid model to test for nonlinear contributions of normalization and contrast saturation. Overall, our findings suggest that the feedforward responses of V1 neurons can be well explained by assuming only a few nonlinear processing stages.

Strength of predicted information content in the brain biases decision behavior

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24-e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain's mechanisms of prediction for perception.

Laminar differences in functional oxygen metabolism in monkey visual cortex measured with calibrated fMRI

Blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI) of cortical layers relies on the hemodynamic response and is biased toward large veins on the cortical surface. Functional changes in the cerebral metabolic rate of oxygen (ΔCMRO2) may reflect neural cortical function better than BOLD fMRI, but it is unknown whether the calibrated BOLD model for functional CMRO2 measurement remains valid at high resolution. Here, we measure laminar ΔCMRO2 elicited by visual stimulation in macaque primary visual cortex (V1) and find that ΔCMRO2 peaks in the middle of the cortex, in agreement with autoradiographic measures of metabolism. ΔCMRO2 values in gray matter are similar as found previously. Reductions in CMRO2 are associated with veins at the cortical surface, suggesting that techniques for vein removal may improve the accuracy of the model at very high resolution. However, our results show feasibility of laminar ΔCMRO2 measurement, providing a physiologically meaningful metric of laminar functional metabolism.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.